Preprocessing is routinely performed to prepare the data for feeding into machine learning models. Preprocessing operations are known to impact the performance of the machine learning models. Some researchers either add noise to input data features [1]}, perform masking [2]} or variable selection [3]}. The operation of masking removes features in the input, improving performance by making the model better at predicting missing features. A similar concept applies when adding noise to the input for training, except the features become relatively noisier but are not replaced entirely. Both these operations can improve the robustness of the model so that it attains good accuracy despite the noise in the training or test data.
